Crimson Desert has the most complex K+M inputs I've ever experienced in my 30+ years of gaming (outside of simulator-style games). This situation is able to be fixed, but I doubt the developers would change their entire control scheme (I'm hoping for a mod to be created). This post is simply to outline K+M controls and some information around those controls/interactions. I've created this because I wanted to change the control scheme for myself, but trying keep track of all the interactions affected by changing a key was madness. I based some of the table style/columns off of this guide, but that guide has errors and inaccuracies.

Notes:

• When changing keybinds, all actions must be bound to a key before the keybinds can be applied.
• Each section cannot contain two actions bound to the same key.

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

A git pattern for keeping markdown context files (docs, specs, decisions, notes) in the same repo as code but on a completely separate history.

An orphan git branch mounted as .context/ via git worktree. Context and code share the same repo but have fully separate histories. Markdown files on the context branch are accessible to both humans and AI agents alongside the codebase, without cluttering the main branch or polluting the code commit log.

The latest builds of Windows 10 and Windows 11 include a build-in SSH server and client that are based on OpenSSH. This means now you can remotely connect to Windows 10/11 or Windows Server 2019 using any SSH client, like Linux distros. Let's see how to configure OpenSSH on Windows 10 and Windows 11, and connect to it using Putty or any other SSH client.

OpenSSH is an open-source, cross-platform version of Secure Shell (SSH) that is used by Linux users for a long time. This project is currently ported to Windows and can be used as an SSH server on almost any version of Windows. In the latest versions of Windows Server 2022/2019 and Windows 11, OpenSSH is built-in to the operating system image.

A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.

This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.

The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.

UniFi is a popular internet service provider in Malaysia, and OpenWrt is a free and open-source firmware for routers that allows for greater control and customization. This tutorial will guide you through the process of configuring UniFi on your OpenWrt router using the web interface.

Connect your computer to the router via Ethernet cable or Wi-Fi, and access the router's web interface by typing the IP address into your web browser's address bar. The default IP address for OpenWrt is 192.168.1.1.

I’ve compiled all the resources that helped me in my GATE preparation, and I hope they help you too! 🚀

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.